13 citations · 32 across the 12 of their papers we have counts for
12 papers
Towards Photonic Band Diagram Generation with Transformer-Latent Diffusion Models
Valentin Delchevalerie, Nicolas Roy, Arnaud Bougaham +3
Photonic crystals enable fine control over light propagation at the nanoscale, and thus play a central role in the development of photonic and quantum technologies. Photonic band d…
SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition
Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier +2
Sign language recognition (SLR) is a machine learning task aiming to identify signs in videos. Due to the scarcity of annotated data, unsupervised methods like contrastive learning…
Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC Loss
Arnaud Bougaham, Benoît Frénay
Anomaly Detection is a crucial step for critical applications such in the industrial, medical or cybersecurity domains. These sectors share the same requirement of handling differe…
On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation
Robin Ghyselinck, Valentin Delchevalerie, Bruno Dumas +1
Numerous studies have recently focused on incorporating different variations of equivariance in Convolutional Neural Networks (CNNs). In particular, rotation-equivariance has gathe…
An Experimental Investigation into the Evaluation of Explainability Methods
Sédrick Stassin, Alexandre Englebert, Géraldin Nanfack +7
EXplainable Artificial Intelligence (XAI) aims to help users to grasp the reasoning behind the predictions of an Artificial Intelligence (AI) system. Many XAI approaches have emerg…
SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks
Valentin Delchevalerie, Alexandre Mayer, Adrien Bibal +1
For many years, it has been shown how much exploiting equivariances can be beneficial when solving image analysis tasks. For example, the superiority of convolutional neural networ…
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks
Arnaud Bougaham, Valentin Delchevalerie, Mohammed El Adoui +1
In this study, a new Anomaly Detection (AD) approach for industrial and medical images is proposed. This method leverages the theoretical strengths of unsupervised learning and the…
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset
Arnaud Bougaham, Mohammed El Adoui, Isabelle Linden +1
In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vi…
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality
Cristina Morariu, Adrien Bibal, Rene Cutura +2
A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all t…
Impact of Legal Requirements on Explainability in Machine Learning
Adrien Bibal, Michael Lognoul, Alexandre de Streel +1
The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes…
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Pieter Delobelle, Paul Temple, Gilles Perrouin +3
Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming u…
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
Adrien Bibal, Benoit Frénay
In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferenc…